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Authors: Lai Wei, Pik Eu Jason Chang, Quentin M. Anstee
Journal: Hepatology (2020)

Potential conflict of interest: Dr. Wei consults for and received grants from AbbVie and Gilead. He consults for Allergan/Tobire, Novartis, Pfizer, Galmed, HistoIndex, and Janssen. He received grants from Bristol‐Myers Squibb. Dr. Anstee consults for, is on the speakers' bureau for, and received grants from Allergan/Tobira. He consults for and is on the speakers' bureau for Genfit SA and Gilead. He consults for and received grants from Novartis and Pfizer. He consults for Acuitas, Blade, BNN Cardio, Cirius, CymaBay, EcoR1, E3Bio, Eli Lilly, Galmed, Grunthal, HistoIndex, Indalo, Imperial Innovations, Intercept, Inventiva, IQVIA, Janssen, Kenes, Madrigal, MedImmune, Metacrine, NewGene, NGM, North Sea, Novo Nordisk, Poxel, ProSciento, Raptor, Servier, and Viking. He is on the speakers' bureau for Bristol‐Myers Squibb, Clinical Care Options, Falk, Fishawack, Integritas, and MedScape. He received grants from AstraZeneca, GlaxoSmithKline, Glympse Bio, and Vertex. He received royalties from Elsevier. We thank Gawrieh et al. for their interest in our work and agree with them that computational methods leveraging machine learning and artificial intelligence approaches offer promise to enhance histopathological assessment of liver biopsies.1 We are grateful to these authors for highlighting the comparison between the r value of individual parameters and that which may be attained when combined indexes are used and are happy to clarify this point. The correlations between histopathological grade/stage and each component of qFIBS (qFibrosis, qInflammation, qBallooning, and qSteatosis) were derived using the sequential feature selection method.2 This automated system selects multiple individual parameters and combines them to derive an optimized model with improved correlation. Thus, the logic of parameter selection was not morphology‐based. In qSteatosis, a total of five parameters were selected, with r values ranging from 0.18 to 0.80, to attain a final r value of 0.802; and in qFibrosis, a total of 17 parameters were selected, with r values ranging from 0.09 to 0.63, to attain a final r value of 0.776. For more challenging histological features, it was necessary to combine a total of 25 parameters by sequential feature selection to attain a final r value of 0.557 for qInflammation and 13 parameters to attain a final r value of 0.533 for qBallooning. Thus, a higher r value may be reached by combining multiple parameters that individually correlate weakly to the histological feature of interest. It is also noteworthy that both positively and negatively correlated parameters may be selected by the sequential feature selection method. In the current study, all five parameters used for qSteatosis were positively correlated (0% negative parameters), and qFibrosis included one negatively correlated parameter out of 17 (6% negative parameters). In contrast, qInflammation included nine negatively correlated parameters out of 25 (36% negative parameters), and qBallooning included five negatively correlated parameters out of 13 (38% negative parameters). We believe the greater difference between the r values of individual parameters and those attained by a combined index may also be related to the complexity derived from the increasing percentage of negatively correlated parameters that were included. Although promising, further refinement of these techniques will be necessary as part of an ongoing program of research.